Overview

Dataset statistics

Number of variables10
Number of observations7503
Missing cells83
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory586.3 KiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

longitude is highly overall correlated with latitude and 1 other fieldsHigh correlation
latitude is highly overall correlated with longitude and 1 other fieldsHigh correlation
total_rooms is highly overall correlated with total_bedrooms and 2 other fieldsHigh correlation
total_bedrooms is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
population is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
households is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
median_income is highly overall correlated with median_house_valueHigh correlation
median_house_value is highly overall correlated with median_incomeHigh correlation
ocean_proximity is highly overall correlated with longitude and 1 other fieldsHigh correlation
total_bedrooms has 82 (1.1%) missing valuesMissing

Reproduction

Analysis started2023-06-12 10:37:07.006142
Analysis finished2023-06-12 10:37:45.137970
Duration38.13 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

Distinct534
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.50729
Minimum-124.35
Maximum-114.55
Zeros0
Zeros (%)0.0%
Negative7503
Negative (%)100.0%
Memory size58.7 KiB
2023-06-12T03:37:45.532573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.3
Q1-121.79
median-118.44
Q3-118.22
95-th percentile-117.9
Maximum-114.55
Range9.8
Interquartile range (IQR)3.57

Descriptive statistics

Standard deviation1.8357769
Coefficient of variation (CV)-0.015361213
Kurtosis-0.66416901
Mean-119.50729
Median Absolute Deviation (MAD)0.45
Skewness-0.68719835
Sum-896663.22
Variance3.370077
MonotonicityIncreasing
2023-06-12T03:37:46.048112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.3 133
 
1.8%
-118.28 131
 
1.7%
-118.29 124
 
1.7%
-118.31 123
 
1.6%
-118.27 120
 
1.6%
-118.25 111
 
1.5%
-118.26 105
 
1.4%
-118.43 97
 
1.3%
-118.44 90
 
1.2%
-118.24 86
 
1.1%
Other values (524) 6383
85.1%
ValueCountFrequency (%)
-124.35 1
 
< 0.1%
-124.3 2
 
< 0.1%
-124.27 1
 
< 0.1%
-124.26 1
 
< 0.1%
-124.25 1
 
< 0.1%
-124.23 3
< 0.1%
-124.22 1
 
< 0.1%
-124.21 3
< 0.1%
-124.19 4
0.1%
-124.18 6
0.1%
ValueCountFrequency (%)
-114.55 1
 
< 0.1%
-114.63 1
 
< 0.1%
-114.65 1
 
< 0.1%
-114.66 1
 
< 0.1%
-114.73 1
 
< 0.1%
-114.98 1
 
< 0.1%
-115.32 1
 
< 0.1%
-115.37 4
0.1%
-115.38 2
< 0.1%
-115.39 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct580
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.608879
Minimum32.67
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2023-06-12T03:37:46.616793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum32.67
5-th percentile33.931
Q134.05
median34.2
Q337.71
95-th percentile39.09
Maximum41.95
Range9.28
Interquartile range (IQR)3.66

Descriptive statistics

Standard deviation1.9905447
Coefficient of variation (CV)0.055900234
Kurtosis-0.65479909
Mean35.608879
Median Absolute Deviation (MAD)0.25
Skewness0.78043287
Sum267173.42
Variance3.9622681
MonotonicityNot monotonic
2023-06-12T03:37:47.170541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.05 200
 
2.7%
34.06 185
 
2.5%
34.04 172
 
2.3%
34.07 171
 
2.3%
34.08 165
 
2.2%
34.1 152
 
2.0%
34.09 149
 
2.0%
34.02 146
 
1.9%
34.03 142
 
1.9%
33.99 138
 
1.8%
Other values (570) 5883
78.4%
ValueCountFrequency (%)
32.67 5
0.1%
32.68 3
 
< 0.1%
32.69 3
 
< 0.1%
32.7 1
 
< 0.1%
32.73 2
 
< 0.1%
32.74 2
 
< 0.1%
32.75 3
 
< 0.1%
32.76 3
 
< 0.1%
32.77 1
 
< 0.1%
32.78 10
0.1%
ValueCountFrequency (%)
41.95 1
< 0.1%
41.92 1
< 0.1%
41.88 1
< 0.1%
41.84 1
< 0.1%
41.81 1
< 0.1%
41.8 2
< 0.1%
41.78 1
< 0.1%
41.77 1
< 0.1%
41.76 1
< 0.1%
41.75 2
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.325337
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2023-06-12T03:37:47.745059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q124
median34
Q341
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.082609
Coefficient of variation (CV)0.3737814
Kurtosis-0.6488065
Mean32.325337
Median Absolute Deviation (MAD)9
Skewness-0.2694662
Sum242537
Variance145.98945
MonotonicityNot monotonic
2023-06-12T03:37:48.414165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 582
 
7.8%
36 380
 
5.1%
35 376
 
5.0%
34 301
 
4.0%
33 255
 
3.4%
37 234
 
3.1%
32 227
 
3.0%
42 204
 
2.7%
39 195
 
2.6%
43 188
 
2.5%
Other values (42) 4561
60.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 9
 
0.1%
3 12
 
0.2%
4 37
0.5%
5 44
0.6%
6 34
0.5%
7 37
0.5%
8 54
0.7%
9 49
0.7%
10 70
0.9%
ValueCountFrequency (%)
52 582
7.8%
51 26
 
0.3%
50 85
 
1.1%
49 76
 
1.0%
48 94
 
1.3%
47 118
 
1.6%
46 140
 
1.9%
45 152
 
2.0%
44 184
 
2.5%
43 188
 
2.5%

total_rooms
Real number (ℝ)

Distinct3656
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2366.7754
Minimum2
Maximum32054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2023-06-12T03:37:49.160984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile588.1
Q11341
median1932
Q32823.5
95-th percentile5548
Maximum32054
Range32052
Interquartile range (IQR)1482.5

Descriptive statistics

Standard deviation1877.7956
Coefficient of variation (CV)0.79339831
Kurtosis32.635209
Mean2366.7754
Median Absolute Deviation (MAD)692
Skewness4.075463
Sum17757916
Variance3526116.4
MonotonicityNot monotonic
2023-06-12T03:37:49.858824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1745 12
 
0.2%
1463 10
 
0.1%
1613 10
 
0.1%
1513 10
 
0.1%
1788 9
 
0.1%
1287 9
 
0.1%
1582 9
 
0.1%
1527 8
 
0.1%
1438 8
 
0.1%
2225 8
 
0.1%
Other values (3646) 7410
98.8%
ValueCountFrequency (%)
2 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
15 1
< 0.1%
18 2
< 0.1%
21 1
< 0.1%
22 2
< 0.1%
24 1
< 0.1%
32 1
< 0.1%
36 2
< 0.1%
ValueCountFrequency (%)
32054 1
< 0.1%
28258 1
< 0.1%
27700 1
< 0.1%
21533 1
< 0.1%
20354 1
< 0.1%
19059 1
< 0.1%
18690 1
< 0.1%
18634 1
< 0.1%
18448 1
< 0.1%
17820 1
< 0.1%

total_bedrooms
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1399
Distinct (%)18.9%
Missing82
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean505.90742
Minimum2
Maximum5290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2023-06-12T03:37:50.873027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile137
Q1286
median411
Q3608
95-th percentile1186
Maximum5290
Range5288
Interquartile range (IQR)322

Descriptive statistics

Standard deviation379.51557
Coefficient of variation (CV)0.75016802
Kurtosis16.994557
Mean505.90742
Median Absolute Deviation (MAD)146
Skewness3.0754402
Sum3754339
Variance144032.07
MonotonicityNot monotonic
2023-06-12T03:37:51.619775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
460 26
 
0.3%
280 25
 
0.3%
399 25
 
0.3%
290 23
 
0.3%
309 23
 
0.3%
246 23
 
0.3%
295 23
 
0.3%
313 22
 
0.3%
289 22
 
0.3%
318 21
 
0.3%
Other values (1389) 7188
95.8%
(Missing) 82
 
1.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 4
0.1%
7 5
0.1%
8 3
< 0.1%
9 3
< 0.1%
10 2
 
< 0.1%
11 4
0.1%
ValueCountFrequency (%)
5290 1
< 0.1%
4457 1
< 0.1%
4183 1
< 0.1%
4179 1
< 0.1%
3984 1
< 0.1%
3864 1
< 0.1%
3493 1
< 0.1%
3298 1
< 0.1%
3179 1
< 0.1%
3079 1
< 0.1%

population
Real number (ℝ)

Distinct2697
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1383.2884
Minimum3
Maximum15507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2023-06-12T03:37:52.380761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile366
Q1789
median1143
Q31692.5
95-th percentile3199
Maximum15507
Range15504
Interquartile range (IQR)903.5

Descriptive statistics

Standard deviation1005.5996
Coefficient of variation (CV)0.72696304
Kurtosis21.282855
Mean1383.2884
Median Absolute Deviation (MAD)415
Skewness3.1748619
Sum10378813
Variance1011230.5
MonotonicityNot monotonic
2023-06-12T03:37:53.003718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1005 16
 
0.2%
788 14
 
0.2%
861 11
 
0.1%
911 11
 
0.1%
825 11
 
0.1%
835 11
 
0.1%
753 11
 
0.1%
850 11
 
0.1%
986 11
 
0.1%
761 11
 
0.1%
Other values (2687) 7385
98.4%
ValueCountFrequency (%)
3 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
20 2
< 0.1%
21 1
< 0.1%
ValueCountFrequency (%)
15507 1
< 0.1%
15037 1
< 0.1%
12203 1
< 0.1%
10988 1
< 0.1%
9671 1
< 0.1%
9427 1
< 0.1%
9135 1
< 0.1%
8997 1
< 0.1%
8907 1
< 0.1%
8768 1
< 0.1%

households
Real number (ℝ)

Distinct1324
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean474.59536
Minimum2
Maximum5050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2023-06-12T03:37:53.694231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile126
Q1272
median388
Q3568.5
95-th percentile1107.9
Maximum5050
Range5048
Interquartile range (IQR)296.5

Descriptive statistics

Standard deviation352.98647
Coefficient of variation (CV)0.74376301
Kurtosis17.293959
Mean474.59536
Median Absolute Deviation (MAD)137
Skewness3.0741277
Sum3560889
Variance124599.45
MonotonicityNot monotonic
2023-06-12T03:37:54.267942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 25
 
0.3%
334 25
 
0.3%
311 25
 
0.3%
380 24
 
0.3%
340 24
 
0.3%
292 24
 
0.3%
329 24
 
0.3%
295 23
 
0.3%
277 23
 
0.3%
269 23
 
0.3%
Other values (1314) 7263
96.8%
ValueCountFrequency (%)
2 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 3
< 0.1%
7 7
0.1%
8 2
 
< 0.1%
9 3
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
13 4
0.1%
ValueCountFrequency (%)
5050 1
< 0.1%
4204 1
< 0.1%
4072 1
< 0.1%
3701 1
< 0.1%
3595 1
< 0.1%
3302 1
< 0.1%
3293 1
< 0.1%
3262 1
< 0.1%
3061 1
< 0.1%
2902 1
< 0.1%

median_income
Real number (ℝ)

Distinct5620
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5970367
Minimum0.4999
Maximum15.0001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2023-06-12T03:37:54.753937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.4625
Q12.2948
median3.1818
Q34.35115
95-th percentile7.13832
Maximum15.0001
Range14.5002
Interquartile range (IQR)2.05635

Descriptive statistics

Standard deviation1.9180808
Coefficient of variation (CV)0.53323915
Kurtosis6.1417389
Mean3.5970367
Median Absolute Deviation (MAD)0.9899
Skewness1.9153888
Sum26988.566
Variance3.6790339
MonotonicityNot monotonic
2023-06-12T03:37:55.251502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.125 24
 
0.3%
2.875 22
 
0.3%
3.875 21
 
0.3%
15.0001 21
 
0.3%
2.625 19
 
0.3%
4.125 17
 
0.2%
3.375 16
 
0.2%
3.25 15
 
0.2%
1.625 15
 
0.2%
4.375 14
 
0.2%
Other values (5610) 7319
97.5%
ValueCountFrequency (%)
0.4999 8
0.1%
0.536 4
0.1%
0.5495 1
 
< 0.1%
0.6775 1
 
< 0.1%
0.6831 1
 
< 0.1%
0.6991 1
 
< 0.1%
0.716 1
 
< 0.1%
0.7286 1
 
< 0.1%
0.7403 1
 
< 0.1%
0.7473 1
 
< 0.1%
ValueCountFrequency (%)
15.0001 21
0.3%
15 1
 
< 0.1%
14.2867 1
 
< 0.1%
13.947 1
 
< 0.1%
13.6842 1
 
< 0.1%
13.5728 1
 
< 0.1%
13.499 1
 
< 0.1%
13.4883 1
 
< 0.1%
13.4196 1
 
< 0.1%
13.2949 1
 
< 0.1%

median_house_value
Real number (ℝ)

Distinct2814
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194794.83
Minimum12
Maximum500001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2023-06-12T03:37:55.758326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile60210
Q1110350
median169300
Q3243200
95-th percentile456210
Maximum500001
Range499989
Interquartile range (IQR)132850

Descriptive statistics

Standard deviation112512.7
Coefficient of variation (CV)0.57759592
Kurtosis0.71889417
Mean194794.83
Median Absolute Deviation (MAD)64600
Skewness1.1034988
Sum1.4615456 × 109
Variance1.2659107 × 1010
MonotonicityNot monotonic
2023-06-12T03:37:56.197144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001 298
 
4.0%
162500 44
 
0.6%
112500 42
 
0.6%
137500 41
 
0.5%
187500 37
 
0.5%
225000 34
 
0.5%
350000 32
 
0.4%
175000 30
 
0.4%
87500 28
 
0.4%
150000 27
 
0.4%
Other values (2804) 6890
91.8%
ValueCountFrequency (%)
12 1
< 0.1%
14999 2
< 0.1%
17500 1
< 0.1%
22500 1
< 0.1%
25000 1
< 0.1%
26600 1
< 0.1%
26900 1
< 0.1%
30000 2
< 0.1%
32500 1
< 0.1%
32900 1
< 0.1%
ValueCountFrequency (%)
500001 298
4.0%
500000 5
 
0.1%
499000 1
 
< 0.1%
498700 1
 
< 0.1%
498600 1
 
< 0.1%
498400 1
 
< 0.1%
497600 1
 
< 0.1%
497400 1
 
< 0.1%
495600 2
 
< 0.1%
495500 1
 
< 0.1%

ocean_proximity
Categorical

Distinct4
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size58.7 KiB
<1H OCEAN
3854 
INLAND
2188 
NEAR BAY
1287 
NEAR OCEAN
 
173

Length

Max length10
Median length9
Mean length7.9765396
Min length6

Characters and Unicode

Total characters59840
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEAR OCEAN
2nd rowNEAR OCEAN
3rd rowNEAR OCEAN
4th rowNEAR OCEAN
5th rowNEAR OCEAN

Common Values

ValueCountFrequency (%)
<1H OCEAN 3854
51.4%
INLAND 2188
29.2%
NEAR BAY 1287
 
17.2%
NEAR OCEAN 173
 
2.3%
(Missing) 1
 
< 0.1%

Length

2023-06-12T03:37:56.517305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T03:37:56.750591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ocean 4027
31.4%
1h 3854
30.1%
inland 2188
17.1%
near 1460
 
11.4%
bay 1287
 
10.0%

Most occurring characters

ValueCountFrequency (%)
N 9863
16.5%
A 8962
15.0%
E 5487
9.2%
5314
8.9%
O 4027
6.7%
C 4027
6.7%
< 3854
 
6.4%
1 3854
 
6.4%
H 3854
 
6.4%
I 2188
 
3.7%
Other values (5) 8410
14.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 46818
78.2%
Space Separator 5314
 
8.9%
Math Symbol 3854
 
6.4%
Decimal Number 3854
 
6.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 9863
21.1%
A 8962
19.1%
E 5487
11.7%
O 4027
8.6%
C 4027
8.6%
H 3854
 
8.2%
I 2188
 
4.7%
L 2188
 
4.7%
D 2188
 
4.7%
R 1460
 
3.1%
Other values (2) 2574
 
5.5%
Space Separator
ValueCountFrequency (%)
5314
100.0%
Math Symbol
ValueCountFrequency (%)
< 3854
100.0%
Decimal Number
ValueCountFrequency (%)
1 3854
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46818
78.2%
Common 13022
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 9863
21.1%
A 8962
19.1%
E 5487
11.7%
O 4027
8.6%
C 4027
8.6%
H 3854
 
8.2%
I 2188
 
4.7%
L 2188
 
4.7%
D 2188
 
4.7%
R 1460
 
3.1%
Other values (2) 2574
 
5.5%
Common
ValueCountFrequency (%)
5314
40.8%
< 3854
29.6%
1 3854
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9863
16.5%
A 8962
15.0%
E 5487
9.2%
5314
8.9%
O 4027
6.7%
C 4027
6.7%
< 3854
 
6.4%
1 3854
 
6.4%
H 3854
 
6.4%
I 2188
 
3.7%
Other values (5) 8410
14.1%

Interactions

2023-06-12T03:37:39.509452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:08.866282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:11.611894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:14.663377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:18.706052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:21.867840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:26.859465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:32.532833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:36.035513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:39.884261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:09.213959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:11.887419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:15.061643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:19.110391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:22.200807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:27.173609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:32.881057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:36.363282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:40.485092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:09.502376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:12.169488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:15.442814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:19.457199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:22.485447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:27.756695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:33.239478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:36.726552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:41.141926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:09.779456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:12.443344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:15.842742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:19.823721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:22.788009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:28.407803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:33.606217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:37.086549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:41.645567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:10.114163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:12.727473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:16.313742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:20.203834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:23.300754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:29.533435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:34.005711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:37.452337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:42.063542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:10.417480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:13.020417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:16.794243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:20.528067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:24.100300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:30.211357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:34.424514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:37.808420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:42.582938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:10.755679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:13.532112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:17.323387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:20.892555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:24.816740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:30.826731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:34.864141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:38.192799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:43.052423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:11.033009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:13.876696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:17.825171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:21.222040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:25.806872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:31.451755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:35.231612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:38.542600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:43.447868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:11.328922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:14.277146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:18.264614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:21.522877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:26.384482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:32.097832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:35.650210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-12T03:37:39.146708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-12T03:37:56.926799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
longitude1.000-0.8390.090-0.0580.0200.1810.042-0.0050.1390.724
latitude-0.8391.000-0.2090.1210.001-0.160-0.0300.068-0.1880.772
housing_median_age0.090-0.2091.000-0.332-0.314-0.282-0.286-0.0880.0980.232
total_rooms-0.0580.121-0.3321.0000.9020.7670.8980.3160.2790.017
total_bedrooms0.0200.001-0.3140.9021.0000.8700.9800.0210.1690.042
population0.181-0.160-0.2820.7670.8701.0000.897-0.0230.0730.088
households0.042-0.030-0.2860.8980.9800.8971.0000.0570.2030.047
median_income-0.0050.068-0.0880.3160.021-0.0230.0571.0000.6490.094
median_house_value0.139-0.1880.0980.2790.1690.0730.2030.6491.0000.331
ocean_proximity0.7240.7720.2320.0170.0420.0880.0470.0940.3311.000

Missing values

2023-06-12T03:37:43.934953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-12T03:37:44.461229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-12T03:37:44.947717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-124.3540.54521820300.08062703.014794600NEAR OCEAN
1-124.3041.80192672552.012984781.979785800NEAR OCEAN
2-124.3041.84172677531.012444563.0313103600NEAR OCEAN
3-124.2740.69362349528.011944652.517979000NEAR OCEAN
4-124.2640.58522217394.09073692.3571111400NEAR OCEAN
5-124.2540.28321430419.04341871.941776100NEAR OCEAN
6-124.2341.75113159616.013434792.480573200NEAR OCEAN
7-124.2340.81521112209.05441723.346250800NEAR OCEAN
8-124.2340.54522694453.011524353.0806106700NEAR OCEAN
9-124.2241.73283003699.015306531.703878300NEAR OCEAN
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
7493-115.3732.82141276270.08672611.937580900INLAND
7494-115.3732.82301602322.011303353.573571100INLAND
7495-115.3732.8132741191.06231691.760468600INLAND
7496-115.3232.8234591139.0327893.6528100000INLAND
7497-114.9833.07181183363.03741273.160757500INLAND
7498-114.7333.4324796243.02271390.896459200INLAND
7499-114.6632.74171388386.07753201.204944000INLAND
7500-114.6532.79214433.064270.857125000INLAND
7501-114.6332.76151448378.09493000.858545000INLAND
7502-114.5532.80192570820.014316081.275056100INLAND